Flyway‐scale GPS tracking reveals migratory routes and key stopover and non‐breeding locations of lesser yellowlegs

Abstract Many populations of long‐distance migrant shorebirds are declining rapidly. Since the 1970s, the lesser yellowlegs (Tringa flavipes) has experienced a pronounced reduction in abundance of ~63%. The potential causes of the species' decline are complex and interrelated. Understanding the timing of migration, seasonal routes, and important stopover and non‐breeding locations used by this species will aid in directing conservation planning to address potential threats. During 2018–2022, we tracked 118 adult lesser yellowlegs using GPS satellite tags deployed on birds from five breeding and two migratory stopover locations spanning the boreal forest of North America from Alaska to Eastern Canada. Our objectives were to identify migratory routes, quantify migratory connectivity, and describe key stopover and non‐breeding locations. We also evaluated predictors of southbound migratory departure date and migration distance. Individuals tagged in Alaska and Central Canada followed similar southbound migratory routes, stopping to refuel in the Prairie Pothole Region of North America, whereas birds tagged in Eastern Canada completed multi‐day transoceanic flights covering distances of >4000 km across the Atlantic between North and South America. Upon reaching their non‐breeding locations, lesser yellowlegs populations overlapped, resulting in weak migratory connectivity. Sex and population origin were significantly associated with the timing of migratory departure from breeding locations, and body mass at the time of GPS‐tag deployment was the best predictor of southbound migratory distance. Our findings suggest that lesser yellowlegs travel long distances and traverse numerous political boundaries each year, and breeding location likely has the greatest influence on migratory routes and therefore the threats birds experience during migration. Further, the species' dependence on wetlands in agricultural landscapes during migration and the non‐breeding period may make them vulnerable to threats related to agricultural practices, such as pesticide exposure.


| INTRODUC TI ON
The Arctic, subarctic, and boreal regions of North America provide habitat for several dozen breeding shorebird species (CHASM, 2006) and nearly all embark on long-distance migrations to the tropical and temperate habitats of southern latitudes during the non-breeding season (Myers et al., 1987). In recent decades, many shorebird populations have experienced steep declines (Stroud et al., 2006;Thomas et al., 2006;Wetlands International, 2012), including ~68% of the 52 shorebird species occurring in North America (Rosenberg et al., 2019). Shorebirds are vulnerable to multiple threats throughout the annual cycle because of their long-distance migrations across various biomes (Piersma et al., 2016;Piersma & Lindström, 2004;Webster et al., 2002) and their reliance on wetland habitats (CHASM, 2006) providing specific food sources (Mathot et al., 2018;Micael & Navedo, 2018).
The causes of North American shorebird declines are complex and include habitat alteration, agrochemical application, urbanization, unregulated harvest, and climate change (Clay et al., 2012;Watts et al., 2015). Some shorebird populations may be more prone to declines due to constraints in their migratory behavior or their geographic distributions (Thomas et al., 2006). For example, the location of stopovers (i.e., locations with abundant food resources) and their relative use during migration may predispose a species or population to a particular threat, leading to an increased risk of decline (Lisovski et al., 2020;Studds et al., 2017). Understanding migratory patterns is an important first step in identifying when and where birds encounter threats and how migratory characteristics (e.g., routes and chronology) may exacerbate population declines and the effectiveness of conservation actions.
The lesser yellowlegs (Tringa flavipes) is a long-distance Nearctic-Neotropical migrant that breeds in the boreal forest of North America. The species has experienced a significant population decline over the past four decades, resulting in a ~63% reduction in abundance (Andres et al., 2012;Bart et al., 2007;Rosenberg et al., 2019). Information gaps exist for this species with respect to migratory routes and timing, stopover and non-breeding locations, and migratory connectivity (MC). Furthermore, it is unclear if lesser yellowlegs from different breeding populations coalesce during the non-breeding period and are exposed to the same threats, or if breeding populations remain separate throughout the annual cycle.
However, recent findings suggest that lesser yellowlegs breeding in Eastern Canada are more likely to migrate through or to jurisdictions in South America and the Caribbean that practice shorebird harvest than Alaskan and Central Canadian breeding populations (McDuffie et al., 2021).
General patterns of lesser yellowlegs migration routes are known, but until recently, an understanding of individual bird movements from geographically distinct breeding populations was lacking. Abundance estimates of shorebirds along the Atlantic Americas Flyway suggest that lesser yellowlegs breeding in Eastern Canada likely stopover in Atlantic Canada and the east coast of the United States on southbound migration (McNeil & Cadieux, 1972). Much less is known about individuals breeding in Alaska and Central Canada, but count data indicate that lesser yellowlegs are common in the Pacific Northwest during southbound migration (Paulson, 1993).
During northbound migration, observational data and a small sample of band recoveries suggest that birds migrate from northern South America, across the Gulf of Mexico, and through the interior plains of the United States (Bent, 1927;Ridgely & Gwynne, 1989;Wunderle et al., 1989). These early reports relied on observations and historic banding and resighting records, which allowed for the description of broad-scale movement patterns, but not information specific to the annual distributions of individuals from different breeding and post-breeding origins. Since these accounts, tracking technology has evolved and the migratory movements of this small shorebird can now be characterized, leading to more informed management and conservation decisions (Bridge et al., 2011). A clear understanding of migratory timing, connectivity, and routes is a missing link for lesser yellowlegs (Tibbitts & Moskoff, 2020), and we addressed this knowledge gap by attaching GPS satellite transmitters to adults and following them throughout their annual cycle. significantly associated with the timing of migratory departure from breeding locations, and body mass at the time of GPS-tag deployment was the best predictor of southbound migratory distance. Our findings suggest that lesser yellowlegs travel long distances and traverse numerous political boundaries each year, and breeding location likely has the greatest influence on migratory routes and therefore the threats birds experience during migration. Further, the species' dependence on wetlands in agricultural landscapes during migration and the non-breeding period may make them vulnerable to threats related to agricultural practices, such as pesticide exposure.

K E Y W O R D S
bird migration, lesser yellowlegs, migratory connectivity, migratory route, stopover, Tringa flavipes

Movement ecology
Our objectives were to synthesize high-resolution data on lesser yellowlegs migratory timing and distance, migratory routes and connectivity, and stopover and non-breeding locations. Because this information is provided in the form of spatially explicit locations, the results of this study can help identify specific locations where lesser yellowlegs encounter probable threats (e.g., habitat alteration and exposure to agrochemicals). These data can assist in developing conservation and management strategies to mitigate threats and slow or reverse current population declines.

| Study locations
We selected seven locations across the lesser yellowlegs' breeding and early-migratory distributions for the deployment of GPS and Labrador and Newfoundland, as determined by GPS tags that transmitted and received for a full annual cycle (n = 6).

| Field methods and tracking summary
We captured adult lesser yellowlegs in Alaska and Central Canada during the incubation and brood-rearing periods (May-July). At locations in Eastern Canada, we captured adults during their presumed southbound migration (July-August). At locations in Alaska and Yellowknife, we used mist nets and chick calls (Johnson et al., 2020) to capture brood-rearing adults. In Churchill, we used shorebird decoys and foraging call recordings to attract lesser yellowlegs. At James Bay and Mingan, we used a combination of mist nets and cannon nets to target foraging and roosting birds. Once captured, we attached to each bird an alphanumeric leg flag, a plastic color band corresponding to the study site, and a USGS metal band. We recorded standard morphometric measurements and collected blood samples to determine sex using molecular markers (Griffiths et al., 2002).
We fit birds with 4.0g PinPoint GPS Argos-75 satellite tags (hereafter, GPS tag; Lotek Wireless) using a modified leg-loop harness (Rappole & Tipton, 1991;Sanzenbacher et al., 2000) made of F I G U R E 1 Map of study locations for seven GPS-tracked populations of lesser yellowlegs. Numbers in parentheses indicate the total number of individual birds with GPS tags per population.

| Tag schedules and processing
We enabled GPS tags to receive and transmit data for the complete annual cycle by selecting schedules that would maximize the amount of data collected while accounting for the estimated battery life of each GPS tag (

| Seasonal periods
Seasonal periods included migration, stopovers, and non-breeding locations of individuals (Table 1). Movements of lesser yellowlegs during the non-breeding period varied among individuals, with some birds remaining relatively sedentary within a small region (i.e., one-degree latitude by one-degree longitude) throughout the nonbreeding period, while others continuously traveling between locations during the non-breeding period. For individuals that did not move >50 km between GPS locations during the full non-breeding period, the non-breeding location was defined as the geographic median of all GPS points received upon arrival to the location and prior to the onset of northbound migration. For individuals that continuously moved throughout the non-breeding period, we defined the non-breeding location as the geographic median of the southernmost cluster of GPS points that were <50 km apart. These nonbreeding locations were used in analyses of migratory distance and connectivity, but not migratory duration because data gaps >14 days were prevalent during the non-breeding season. Non-breeding locations could only be determined for birds whose GPS tag continued to transmit data through 23 November. We chose 23 November because this date marks the beginning of the non-breeding period when large-scale movements of lesser yellowlegs are less frequent, based on relative abundance estimates from the species account in "The Birds of the World" (Tibbitts & Moskoff, 2020) and records in eBird (Fink et al., 2020).

| Statistical analyses
The GPS tags used in this study recorded locations at intervals >24 h (Table S1). Therefore, we used foieGras (version 0.4.0) in program R (R Core Team, 2022) to interpolate data gaps in migratory routes by generating a continuous state-space random walk model for each individual bird (Jonsen et al., 2019). A random walk model portrays stochastic movements that are uncorrelated and unbiased (Codling et al., 2008). The foieGras model uses this principle to estimate locations that correspond to real observations transmitted by each GPS tag. This model requires that location data include the following parameters: location class, semi-major and semi-minor axis of an ellipse, and an ellipse error value. The location class indicates the total number of messages received per satellite pass and the location accuracy. The error ellipse indicates the estimated distance

Southbound migration
The period between departure from the breeding site and arrival at the terminal non-breeding location

Northbound migration
The period between departure from the non-breeding location and arrival at the breeding location Non-breeding The period between the termination of southbound migration and the commencement of northbound migration Stopover A location with adequate food resources and environmental conditions where a bird stops migrating for ≥2 days and travels <50 km between any two locations Breeding departure date Date of a bird's last occurrence at a breeding location prior to traveling in a unidirectional trajectory of >100 km TA B L E 1 Definitions for seasonal periods and dates of GPS-tracked lesser yellowlegs between 2018 and 2022 (i.e., semi-major and semi-minor axes) that the estimated location is from the actual location. All the location data used in the foieGras model were GPS derived; therefore, the location class was set as "3D" (i.e., indicating that the data are GPS and not Doppler derived).
Additionally, because the error around GPS locations is considered minimal, we used an error ellipse of 0 and 300 m as the semi-major and semi-minor axis values, respectively . Next, we ran the location data through a pre-filter and fit the model to estimate a location every 24 h using an estimated flight veloc- Additionally, we compared the fit of negative binomial and Poisson distribution models and determined that the negative binomial model was the best fit for the available data. Models including all possible additive combinations of non-correlated covariates were compared using AIC c . We did not conduct this analysis for northbound migration due to limited sample size and incomplete data on departure dates and body mass. Furthermore, James Bay and Mingan birds were excluded because they were captured during southbound migration.

| Migratory routes and connectivity
We visualized the migration routes of individuals by plotting GPS locations and foieGras estimated model locations using the Mercator projection in ArcMap 10.6 (ESRI, 2018). Track lines between consecutive locations were plotted using the "XY to lines" tool, which created a geodesic line that most accurately represents the shortest distance between two points on the Earth's surface. Next, we divided track lines into southbound and northbound migration based on the definitions of migratory seasons (Table 1).
Migratory connectivity refers to the correlation of distances

| Stopover and non-breeding locations
We identified the relative importance of stopover and non-breeding locations using the number of unique individuals that stopped within a geographic area. We defined stopover locations based on the duration of stay and distance traveled between consecutive locations (

| RE SULTS
We deployed 118 GPS tags on adult lesser yellowlegs (52 males, 60 females, and 6 of unknown sex; Table S2). Variability in GPS-tag schedules across years, intermediate lapses in satellite communication, and the presumed mortality of some birds resulted in the partial loss of functional data during migratory periods. Fifty-two GPS tags provided data on breeding departure dates and full migratory track lines through the non-breeding period, which is required to determine southbound migratory distance. One hundred and fifteen tags provided full or partial information on migratory routes, while the remaining three GPS tags failed to transmit/receive at all. Sixtyeight tags were used to calculate MC because these tags transmitted and received locations through November 23 each year (see "Section 2.4" for a description of migratory seasons). Finally, 90 tags were used for stopover and non-breeding location determination because 28 tags failed to transmit/receive prior to the first observed stopover.

| Breeding departure dates and migratory distance
Departure dates from breeding locations were earlier for Alaskan than Central Canadian populations. Also, females departed earlier than males for Alaska, while departure dates were similar for males and females breeding in Central Canada (Figure 2). However, 95% confidence intervals for the capture longitude estimate overlapped zero; therefore, the only clear predictor of migratory distance was body mass, with heavier birds migrating greater distances ( Figure 3).

| Migratory routes and connectivity
Adult lesser yellowlegs originating in Alaska and Central Canada followed similar migratory routes through the Midcontinent Americas  Figure 5). All birds tagged in Mingan TA B L E 2 Relationship between migratory distance and spatial and biological covariates: Sex, capture mass, and capture (GPS deployment) longitude. Models were fit using a binomial generalized linear model and Akaike's information criterion for model selection.

F I G U R E 3
Scatter plot showing the relationship of southbound migratory distance (km) and body mass (g) of lesser yellowlegs from Alaskan and Central Canadian populations. The blue and red lines represent the fitted linear regression lines for Alaska and Central Canada and the gray bands represent the 95% confidence interval bands. Alaskan population (n = 34; 20 females and 14 males) and Central Canadian population (n = 16; 7 females and 9 males).
The 68 individuals tracked to non-breeding locations from different breeding origins exhibited weak migratory connectivity (mean MC = 0.174 ± 0 SE). Our calculation of MC yielded a standard error of zero because the GPS locations used in the estMC function are highly precise. This connectivity value suggests that mixing among populations occurs during the non-breeding period.

| Stopover and non-breeding locations
The

| DISCUSS ION
Through the use of miniaturized GPS tracking technology, we found that lesser yellowlegs' departure timing from breeding locations corresponded to sex and breeding population, such that birds originating in Alaska departed earlier than those originating in Central Canada and females from Alaska departed earlier than males. Total migratory distance did not vary as a function of sex, but we observed a pronounced effect of body mass, with heavier individuals migrating greater distances. Finally, MC was weak with substantial mixing of birds from different breeding origins during the non-breeding season.

Females departed breeding locations earlier than males in
Alaska, which is consistent with the observation that male lesser yellowlegs typically remain with broods longer than females (Tibbitts & Moskoff, 2020). This later departure from breeding locations potentially influences a males' ability to successfully migrate. For example, the depletion in prey abundance during the temporal progression of southbound migration has been documented in shorebird foraging areas in North America and this has the potential to induce energy deficits in late-arriving birds (Schneider & Harrington, 1981). Also, birds departing breeding locations later must not only align migration phenology with prey availability (Colwell & Landrum, 1993;Newton, 2007) but also avoid or minimize overlap with the migrations of predators. Many Arctic-breeding shorebirds migrate in July and August, prior to the migration of hawks and falcons, but a delay in migration (e.g., late hatch and prolonged brood care) could put male and juvenile shorebirds at risk of aligning their migration with avian predators (Lank et al., 2003). Local environmental conditions may also influence departure dates and how they differ among sexes. Post-breeding shorebirds in the subarctic are known to select tailwinds that support migratory efficiency (Duijins et al., 2019). Due to the unique seasonal oceanic and atmospheric conditions in the Hudson Bay Region (Ridenour et al., 2019), birds departing Churchill in late summer may experience more variable wind patterns compared to the Alaskan population, potentially minimizing the window of favorable wind conditions and requiring sexes to depart at similar times. This concept, however, requires further investigation.
Body mass at time of capture was the best predictor of total southbound migratory distance. Birds were captured at the end of incubation and early brood rearing, suggesting that measured body masses represented the minimum potential body condition of each bird prior to southbound migration. The body size hypothesis argues that heavier birds can survive longer periods of fasting (Duijns et al., 2017;Ketterson & Nolan, 1976); therefore, light birds in poor condition may stop earlier to replenish reserves than birds in good condition. Fueling rates in shorebirds can vary across latitudinal gradients (Reneerkens et al., 2020). In the Northern and Southern Hemispheres, fuel-loading rates (i.e., feed intake) decrease from high to low latitudes (Aharon-Rotman et al., 2016;Piersma et al., 2005;Williams et al., 2007), suggesting that lesser yellowlegs that migrate to more southern latitudes of South America may be better able to replenish fat reserves. Additionally, foraging rates can be constrained in tropical regions near the equator due to physiological stress from high ambient temperatures (Battley et al., 2003;Speakman & Król, 2010). Tropical non-breeding regions have also been linked to low adult survival of Arctic-breeding shorebirds (Reneerkens et al., 2020).
Despite differences in departure timing and migratory distance, with weak MC and geographically expansive non-breeding ranges may be less susceptible to population declines than birds with strong MC and restricted non-breeding ranges (Gilroy et al., 2016).
For example, species with strong MC may be restricted to few nonbreeding locations and may therefore be more susceptible to habitat loss or degradation of those locations (Dolman & Sutherland, 1995).
Additionally, populations that experience weak MC may have considerable genetic variation in migratory behavior, which may facilitate adaptation to environmental changes (Webster & Marra, 2005).
Yet despite the possible benefits conveyed on lesser yellowlegs by weak MC, the species is declining, suggesting that threats are both pervasive and acute to overpower any benefits associated with weak connectivity. Also, the dependence on certain regions (e.g., Prairie Potholes, Argentine Pampas, and Mississippi Alluvial Plain) by a large proportion of tagged individuals highlights the species' vulnerability to threats in these regions.
Our consists of thousands of small wetlands, ponds, lakes, and floodplains surrounded by crop fields that provide foraging habitat for refueling migratory lesser yellowlegs. Between 1997 and 2009, 39% of emergent wetland loss in the PPR of the United States was attributed to agricultural conversion, and of the remaining wetlands, 94% were located adjacent to, or within, crops or pasturelands (Dahl, 2014;Muhammad et al., 2018). Like the PPR, the Argentine Pampas comprises expansive grasslands, pasture, and agricultural land surrounded by wetlands, floodplains, and tributaries. The recent increased demand for soybean exports has resulted in the conversion of free-range cattle pasture and wetlands to cropland and concentrated cattle feedlots (Rossi, 2015). The Mississippi Alluvial Plain (MAP) in the United States encompasses swamps, bayous, and hundreds of kilometers of rivers. Historically, the region consisted of 9 million hectares of wetland forests, but today only less than a quarter of forests remain following the construction of flood-control levees and the conversion to agricultural lands (Hoyle, 2015). The MAP region yields the highest returns for aquaculture (i.e., catfish) and rice in the United States (U.S. Department of Agriculture National Agricultural Statistics Service, 2019) and helps drive the regional economy (Alhassan et al., 2019).
The effects of agricultural practices on lesser yellowlegs are a concern across the migratory and non-breeding range of the species and warrant further investigation. In the PPR, agricultural pesticides, herbicides, and fungicides are applied in large quantities and are known to accumulate in wetlands (Goldsborough & Crumpton, 1998;Malaj et al., 2020;McMurry et al., 2016).  (Ballesteros et al., 2014). At the mouth of the Mississippi River, pesticide accumulation from agricultural production in the Mississippi Alluvial Plain results in annual hypoxic conditions (Rabalais et al., 2002) and harmful algae bloom in coastal salt marshes, in turn adversely affecting the food web (Ning et al., 2015) and shorebird foraging behavior (Kvitek & Bretz, 2005).
Habitat conversion to agriculture and, consequently, pesticide application are prevalent in regions that support high densities of lesser yellowlegs. Whether in freshwater systems in the PPR and Argentine Pampas, or marine-influenced systems at the mouth of the Mississippi River, further investigations are needed to diagnose contaminant loads carried by the species and the effects of contaminants and habitat loss on productivity and survival.

| CON CLUS ION
The lesser yellowlegs population is in decline and understanding their migratory ecology is the crucial first step in evaluating the species' vulnerability to potential threats. Our study suggests that lesser yellowlegs from disparate breeding populations are vulnerable to similar threats throughout migration and the non-breeding period in relation to migratory pathways and connectivity.
By identifying the distributions of several lesser yellowlegs populations using GPS-tracking technology, we can start to identify specific regions where more information could be sought to inform conservation actions. For example, working with agricultural experts in understanding possible contaminants and any effects on the productivity and survival of lesser yellowlegs would increase our collective knowledge regarding the effect of agricultural practices on refueling shorebirds. Additionally, future studies could use GPS tags with more frequent fixes to determine more precise migration phenology, including stopover durations and hence key staging locations for targeted conservation efforts.

ACK N OWLED G M ENTS
We thank the biologists, technicians, and volunteers who worked tirelessly to identify lesser yellowlegs breeding pairs across